In this paper, a Model Predictive Control (MPC)-based approach for vineyard spraying is presented, able to adapt to different vine row structures and suitable for real-time applications. In the presented approach, the mobile base moves along a row of vines while the robotic arm controls the position and orientation of the spray nozzle. A reference lawnmower pattern trajectory is generated from the vine canopy description, with the aim of minimizing waste while ensuring vine coverage. MPC is used to compute the trajectory of the vehicle along the row and the manipulator tool trajectory, which follow the spray reference, while minimizing vehicle acceleration and tool displacement. The manipulator tool velocity commands provided by the MPC algorithm are tracked using task space control. The presented approach is evaluated in two experiments: a vineyard spraying scenario and an external evaluation scenario in an indoor environment equipped with the Optitrack camera system.

There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed.

This paper presents the overview and preliminary results of the HEKTOR - Heterogeneous Autonomous Robotic System in Viticulture and Mariculture project. HEKTOR is divided into two main parts, each dealing with specific scenarios in viticulture and mariculture. The robots used in the project and each specific scenario considered are presented. In viticulture, this includes vineyard surveillance, spraying and bud rubbing using an all-terrain mobile manipulator and unmanned aerial vehicle (UAV). In mariculture, scenarios include coordinated monitoring of fish net cages from below the surface and from the air, using the UAV, an unmanned surface vehicle (USV) and a remotely operated underwater vehicle (ROV).

This paper deals with a problem of precise jumping for legged robots: what are the control inputs required to perform a jump that results in a desired landing point? We propose a novel precise jump planning method, formulated as a bilevel optimization problem. The presented formulation exploits certain insights into the jump dynamics, leading to a low-dimensional optimization problem, and allowing fast computation. During the Flight phase of a jump there are no external forces other than gravity acting on the robot, so its centroidal angular momentum (CAM) is conserved, and its center of mass (COM) follows a ballistic trajectory. This trajectory depends solely on COM position and velocity at Liftoff. We define a bilevel optimization problem consisting of a nonlinear upper-level problem, and a lower-level quadratic programming (QP) problem. The upper-level problem selects COM position and velocity at Liftoff that result in a desired landing point, while minimizing CAM at Liftoff. The lower-level problem selects ground reaction forces during Push-Off that achieve desired COM position and velocity at Liftoff. The results are presented on a simulated one-legged robot, but the proposed approach can be extended to bipeds and multi-legged robots.

In this paper, we present a direct drive brush-shaped tool developed for the use of robotic vine suckering. Direct drive design philosophy allows for precise and high bandwidth control of the torque exerted by the brush. Besides limiting the torque exerted onto the plant, this kind of design philosophy allows the brush to be used as a torque sensor. High bandwidth torque feedback from the tool is used to enable a position controlled robot arm to perform the suckering task without knowing the exact position and shape of the trunk of the vine. An experiment was conducted to investigate the dependency of the applied torque on the overlap between the brush and the obstacle. The results of the experiment indicate a quadratic relationship between torque and overlap. This quadratic function is estimated and used for compliant trunk shape following. A trunk shape following experiment demonstrates the utility of the presented tool to be used as a sensor for compliant robot arm control. The shape of the trunk is estimated by tracking the motion of the robot arm during the experiment.